solprop.backends.numba_cpu.envelope_plasma#

Own compiled envelope-current workspaces and their forward transforms.

Functions

make_source_column(n_pol, *, parallel[, ...])

Compile a deterministic source pass over independent physical nodes.

Classes

EnvelopeWorkspace(assembly, shape, *, ...)

Preallocate channel histories, current projection buffers and FFT plans.

class solprop.backends.numba_cpu.envelope_plasma.EnvelopeWorkspace(assembly, shape, *, embedded, parallel, flag, threads)[source]#

Preallocate channel histories, current projection buffers and FFT plans.

Parameters:

assemblyEnvelopePlasmaAssembly: Validated channels and spectral multipliers.
shapetuple of int: Chunk nodes, node components, temporal columns and fine samples.
embeddedbool: Allocate the additional embedded-rule source projections.
parallelbool: Parallelise source evaluation across physical nodes.
flagstr: Existing FFTW planning policy.
threadsint: Existing modal FFT thread count.

assemble(destination, crop, *, coarse=False)[source]#

Transform both current streams and add their masked spectral sources.

Parameters:

destinationnumpy.ndarray: Ordinary polarisation fine spectrum, updated in place.
cropnumpy.ndarray: Coarse-to-fine bin mapping.
coarsebool: Transform embedded spatial projections for the quadrature alarm.

memory_ledger()[source]#

List each owned array once, including full-width partial-chunk padding.

Returns:

dict: Named persistent buffer sizes in bytes; no arrays are allocated by a final partial chunk or an RHS call.

populations(field, initials, dt, window, output)[source]#

Scan one statistics chunk into preallocated channel/node partials.

Parameters:

fieldnumpy.ndarray: Physical V/m envelopes, shape (n_nodes*n_pol, n_to).
initialstuple of float: Initial fractions in channel order at the requested plane.
dtfloat: Fine time spacing in s.
windownumpy.ndarray: Read-only source collar; unused by this statistics specialization.
outputnumpy.ndarray: Output rows alternate final fraction and peak cycle-averaged rate in s^-1 for each channel, columns are nodes in this chunk.

Raises:

project(projection, embedded, n_modes, *, first, count)[source]#

Accumulate this chunk’s two currents before temporal transformation.

Parameters:

projectionnumpy.ndarray: Already scaled projection columns for the active nodes.
embeddednumpy.ndarray or None: Corresponding embedded-rule projection.
n_modesint: Output mode count.
firstbool: Overwrite on the first chunk and accumulate later chunks in order.
countint: Active node-component rows, including a final partial chunk.

respond(field, scalars, dt, window)[source]#

Recompute independent channel histories for one current RHS chunk.

Parameters:

fieldnumpy.ndarray: Physical V/m fields, rows node*component, columns fine time.
scalarsStageScalars: Current position’s channel densities and initial populations.
dtfloat: Fine time spacing in s.
windownumpy.ndarray: Read-only material-source collar.

Raises:

ConvergenceError: If any node fails angular convergence; the driver sees this before a source enters a forward transform.

solprop.backends.numba_cpu.envelope_plasma.make_source_column(n_pol, *, parallel, statistics=False)[source]#

Compile a deterministic source pass over independent physical nodes.

Parameters:

n_polint: Cartesian components per node, one or two.
parallelbool: Parallelise independent nodes, preserving each temporal scan’s order.
statisticsbool, optional: Only fill population histories; omit current formation.

Returns:

object: Explicitly typed Numba source kernel; all work buffers are supplied.